Principles of visual information retrieval
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| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Springer
2001
|
| Schriftenreihe: | Advances in pattern recognition
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
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| 245 | 1 | 0 | |a Principles of visual information retrieval |c Michael S. Lew (ed.) |
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|---|---|
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| adam_text |
Contents
List of Contributors xvii
PART I: Fundamental Principles 1
1. Visual Information Retrieval: Paradigms,
Applications, and Research Issues
Michael S. Lew and Thomas S. Huang 3
1.1 Introduction 3
1.2 Retrieval Paradigms 3
1.3 Applications 5
1.3.1 Architecture, Real Estate, and Interior Design 6
1.3.2 Biochemical 6
1.3.3 Digital Catalog Shopping 6
1.3.4 Education 7
1.3.5 Film and Video Archives 7
1.3.6 Medicine 7
1.4 Research Issues 7
1.5 Summary 9
References 9
2. Color Based Retrieval
Theo Gevers 11
2.1 Introduction 11
2.2 Color Fundamentals 13
2.3 Color Appearance 15
2.3.1 The Light Source 16
2.3.2 The Object 18
2.3.3 The Observer 19
2.4 Colorimetry 24
2.4.1 XYZ System 24
2.4.2 RGB System 27
2.4.3 HSI System 28
2.4.4 YIQ and YUV Systems 29
x Contents
2.4.5 Color Order Systems 30
2.5 Color Invariance 32
2.5.1 Reflection from Inhomogeneous Dielectric Materials. 33
2.5.2 Reflectance with White Illumination 34
2.5.3 Color Constancy 37
2.6 Color System Taxonomy 39
2.6.1 Gray Value System 39
2.6.2 RGB Color System 40
2.6.3 rgb Color System 40
2.6.4 ATZ Color System 41
2.6.5 xyz Color System 42
2.6.6 U*V*W* Color System 42
2.6.7 L*a*b* Color System 43
2.6.8 71/2/3 Color System 44
2.6.9 YIQ and YUV Color Systems 45
2.6.10 HSI Color System 45
2.6.11 Color Ratios 46
2.7 Color and Image Search Engines 47
2.8 Conclusion 47
References 48
3. Texture Features for Content Based Retrieval
Nicu Sebe and Michael S. Lew 51
3.1 Introduction 51
3.1.1 Human Perception of Texture 52
3.1.2 Approaches for Analyzing Textures 54
3.2 Texture Models 55
3.2.1 Parametric PDF Methods 57
3.2.2 Non Parametric PDF Methods 62
3.2.3 Harmonie Methods 66
3.2.4 Primitive Methods 68
3.2.5 Fractal Methods 72
3.2.6 Line Methods 74
3.2.7 Structural Methods 75
3.3 Texture in Content Based Retrieval 76
3.3.1 Texture Segmentation 77
3.3.2 Texture Classification and Indexing 79
3.3.3 Texture for Annotation 80
3.4 Summary 81
References 82
4. State of the Art in Shape Matching
Remco C. Veitkamp and Michiel Hagedoorn 87
4.1 Introduction 87
Contents xi
4.2 Approaches 89
4.2.1 Global Image Transforms 90
4.2.2 Global Object Methods 90
4.2.3 Voting Scheines • • 93
4.2.4 Computational Geometry 94
4.3 Finite Point Sets 97
4.3.1 Bottleneck Matching 98
4.3.2 Minimum Weight Matching 99
4.3.3 Uniform Matching 99
4.3.4 Minimum Deviation Matching 99
4.3.5 Hausdorff Distance 99
4.3.6 Transformation Space Subdivision 100
4.4 Curves 100
4.4.1 Turning Function 102
4.4.2 Signature Function 103
4.4.3 Affine Arclength 104
4.4.4 Reflection Metric 104
4.4.5 Hausdorff Distance 105
4.4.6 Frechet Distance 106
4.4.7 Size Function 107
4.4.8 Pixel Chains 107
4.5 Regions 107
4.5.1 Turning Function 107
4.5.2 Frechet Distance 108
4.5.3 Hausdorff Distance 109
4.5.4 Area of Overlap and Symmetrie Difference 111
4.6 Robustness 112
4.7 Software 114
References 115
5. Feature Similarity
Jean Michel Jolion 121
5.1 Introduction 121
5.2 From Images to Similarity 121
5.2.1 Pre attentive vs Attentive Features and Similarities . 121
5.2.2 Distance vs. Similarity 122
5.3 From Features to Similarity 125
5.3.1 Complete vs. Partial Feature 125
5.3.2 Global vs. Local Feature 126
5.3.3 About Histograms 127
5.3.4 Global vs. Individual Comparison of Features 129
5.4 Similarity Between Two Sets of Points 130
5.4.1 Multidimensional Voting 130
5.4.2 Graph Based Matching 131
xii Contents
5.5 Similarity Between Histograms 133
5.5.1 Classic Distances 133
5.5.2 The Unfolded Distance 135
5.5.3 The Earth Mover's Distance 135
5.6 Merging Similarities 137
5.6.1 An Information Retrieval Approach 137
5.6.2 A Probabilistic Model 138
5.7 Similarity Improvement 139
5.8 Conclusions 141
Fteferences 141
6. Feature Selection and Visual Learning
Michael S. Lew 145
6.1 Introduction 145
6.2 Simple Semantics 145
6.3 Complex Backgrounds 146
6.4 Feature Selection 147
6.4.1 Class Separability 148
6.4.2 Probabilistic Separation 148
6.4.3 Mahalanobis Distance 149
6.4.4 Optimal Search 150
6.4.5 Sequential Forward Selection (SFS) 151
6.4.6 Generalized Sequential Forward Selection (GSFS) 151
6.4.7 Sequential Backward Selection (SBS) 152
6.4.8 Generalized Sequential Backward Selection (GSBS) . 152
6.4.9 Plus L Take Away R Selection (LRS) 152
6.4.10 Floating Methods 152
6.4.11 Stochastic Methods 153
6.4.12 Neural Network Methods 154
6.4.13 Direct Methods 154
6.4.14 Summary of the Feature Selection Algorithms 155
6.5 Case Studies on Performance of Feature Selection Methods . 155
6.6 Content Based Retrieval 156
6.7 Discussion 159
6.8 Summary 160
References 161
7. Video Indexing and Understanding
Michael S. Lew, Nicu Sebe, and Paul C. Gardner 163
7.1 Introduction 163
7.1.1 Video Query Formulation 164
7.1.2 Video Categorization 164
7.1.3 Searching 165
7.1.4 Browsing 167
Contents xiii
7.1.5 Viewing 168
7.2 Video Analysis and Processing 168
7.2.1 Video Shots 168
7.2.2 Video Stories 189
7.2.3 Video Parsing 190
7.2.4 Video Summaries 191
7.2.5 Automatic Attributes Generation for Video 191
7.3 Discussion 192
References 193
PART II: Advanced Topics 197
8. Query Languages for Multimedia Search
Shi Kuo Chang and Erland Jungert 199
8.1 Introduction 199
8.2 Query Languages for Multimedia Databases 201
8.3 Query Languages for Heterogeneous Multimedia Databases . 202
8.4 The a Query Language 203
8.4.1 Basic Concepts 204
8.4.2 A General 7 Operator for a Queries 206
8.4.3 The £QL Query Language 208
8.4.4 Querying for Multisensor Data Fusion 209
8.5 Discussion 215
References 215
9. Relevance Feedback Techniques in Image Retrieval
Yong Rui and Thomas S. Huang 219
9.1 Introduction 219
9.2 The Retrieval Process Based on Relevance Feedback 222
9.3 Parameter Updating: a Heuristic Approach 224
9.3.1 Update of the Query Vector qi 224
9.3.2 Update of tu* 225
9.3.3 Update of u 226
9.4 Parameter Updating: Optimal Approaches 227
9.4.1 Parameter Update at the Representation Level 227
9.4.2 Practical Considerations 230
9.5 Parameter Update at Both Levels: Optimal Approaches 231
9.5.1 Quadratic in both £() and #*() 231
9.5.2 Linear in #() and Quadratic in #;() 237
9.6 Case Studies 243
9.6.1 Data Sets 243
9.6.2 Experiments for Algorithms in Sections 9.3.1 and 9.3.2 244
9.6.3 Experiments for Algorithm in Section 9.3.3 245
9.6.4 Experiments for Algorithms in Section 9.5.2 253
xiv Contents
9.7 Conclusions 254
9.8 Acknowledgment 256
References 257
10. Mix and Match Features in the ImageRover
Search Engine
Stan Sclaroff, Marco La Cascia, Saratendu Sethi,
and Leonid Taycher 259
10.1 Introduction 259
10.2 Relevance Feedback: Background 261
10.3 Unified Feature Representation 262
10.3.1 Textual Statistics 262
10.3.2 Visual Statistics 264
10.3.3 Combined Vector Space 267
10.3.4 Dimensionality Reduction 267
10.4 Relevance Feedback 268
10.4.1 Mixing Features and Distance Metrics 269
10.4.2 Relevance Feedback Algorithm 270
10.5 System Implementation 271
10.5.1 User Interface 271
10.5.2 Example Search 271
10.6 Summary 273
References 275
11. Integrating Analysis of Context and Image Content
Vassilis Athitsos, Charles Frankel, and Michael J. Swain 279
11.1 Introduction 279
11.2 Extracting Relevant Text 279
11.2.1 Relevant Text in HTML Documents 280
11.2.2 Other Document Formats 282
11.3 Image Classification: Photographs vs. Graphics 282
11.3.1 What are Photographs and Graphics? 283
11.3.2 Differences Between Photographs and Graphics 283
11.3.3 Image Metrics 284
11.3.4 Constructing the Decision Trees 288
11.3.5 Preparation of Training and Testing Sets 288
11.3.6 Reasons for Using Multiple Decision Trees 289
11.3.7 Results 290
11.3.8 Improved Learning Techniques 291
11.3.9 Image Context Metrics 291
11.3.10 Mixed Images 292
11.3.11 Other Approaches 293
11.4 Other Types of Image Classification 294
11.4.1 Detecting Portraits 294
Contents xv
11.4.2 Distinguishing Color from Gray Scale Images 294
11.4.3 Filtering Pornographic Images 294
11.5 User Interface Issues in Image Search 295
References 295
12. Semantic Based Retrieval of Visual Data
Clement Leung, Simon So, Audrey Tarn, Dwi Sutanto,
and Philip Tse 297
12.1 Introduction 297
12.2 Two Kinds of Visual Contents 297
12.2.1 Primitive Contents 297
12.2.2 Complex Contents 298
12.2.3 Levels of Complex Contents 298
12.3 Multiple Search Space Pruning 299
12.4 Using Feature Vectors for Primitive Content Search 303
12.5 Inverted Image Indexing Using Composite Bitplane Signature306
12.6 Using Semantic Data Models for Complex Content Search . 309
12.6.1 The Basic Ternary Fact Model 310
12.6.2 Natural Language Indexing 311
12.6.3 Logical and Physical Index Structure 312
12.7 Discussion and Future Work 313
References 316
13. Trademark Image Retrieval
John P. Eakins 319
13.1 Introduction 319
13.2 Trademark Search and Registration 320
13.2.1 General Principles 320
13.2.2 Searching for Word Marks 320
13.2.3 Searching for Device (Image) Marks 321
13.2.4 Other Applications of Trademark Image Matching . 323
13.2.5 System Requirements for Trademark Image Retrieval. 323
13.3 Techniques for Image Retrieval 325
13.3.1 The Growth of CBIR 325
13.3.2 Techniques for Shape Retrieval 325
13.3.3 Other Types of Image Retrieval Techniques 329
13.3.4 Retrieval Efficiency 330
13.3.5 Effectiveness of CBIR Techniques 331
13.4 Trademark Image Retrieval Systems 333
13.4.1 System Design Issues 333
13.4.2 Manually Based Techniques for Similarity Retrieval . 334
13.4.3 CBIR Techniques for Similarity Retrieval 334
13.4.4 Logo Recognition 343
13.5 Conclusions 344
xvi Contents
13.5.1 Effectiveness of Current Techniques 344
13.5.2 Future Prospects 345
References 346
Author Index 351
Subject Index 353 |
| any_adam_object | 1 |
| author_GND | (DE-588)122235088 |
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| discipline | Allgemeines Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| indexdate | 2025-01-02T18:25:10Z |
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| isbn | 1852333812 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009175415 |
| oclc_num | 247528706 |
| open_access_boolean | |
| owner | DE-20 DE-473 DE-BY-UBG DE-739 DE-29T DE-91G DE-BY-TUM DE-523 DE-634 DE-11 DE-525 |
| owner_facet | DE-20 DE-473 DE-BY-UBG DE-739 DE-29T DE-91G DE-BY-TUM DE-523 DE-634 DE-11 DE-525 |
| physical | XIX, 356 S. Ill., graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Springer |
| record_format | marc |
| series2 | Advances in pattern recognition |
| spellingShingle | Principles of visual information retrieval Beeldverwerking gtt Infographie ram Information storage and retrieval gtt Lectures optiques ram Patroonherkenning gtt Processamento de imagens larpcal Traitement d'images - Techniques numériques ram appariement inriac couleur inriac indexation image inriac ombre inriac recherche image inriac texture inriac traitement image inriac Image processing Digital techniques Optical scanners Information Retrieval (DE-588)4072803-1 gnd Visualisierung (DE-588)4188417-6 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4072803-1 (DE-588)4188417-6 (DE-588)4006684-8 (DE-588)4143413-4 |
| title | Principles of visual information retrieval |
| title_auth | Principles of visual information retrieval |
| title_exact_search | Principles of visual information retrieval |
| title_full | Principles of visual information retrieval Michael S. Lew (ed.) |
| title_fullStr | Principles of visual information retrieval Michael S. Lew (ed.) |
| title_full_unstemmed | Principles of visual information retrieval Michael S. Lew (ed.) |
| title_short | Principles of visual information retrieval |
| title_sort | principles of visual information retrieval |
| topic | Beeldverwerking gtt Infographie ram Information storage and retrieval gtt Lectures optiques ram Patroonherkenning gtt Processamento de imagens larpcal Traitement d'images - Techniques numériques ram appariement inriac couleur inriac indexation image inriac ombre inriac recherche image inriac texture inriac traitement image inriac Image processing Digital techniques Optical scanners Information Retrieval (DE-588)4072803-1 gnd Visualisierung (DE-588)4188417-6 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Beeldverwerking Infographie Information storage and retrieval Lectures optiques Patroonherkenning Processamento de imagens Traitement d'images - Techniques numériques appariement couleur indexation image ombre recherche image texture traitement image Image processing Digital techniques Optical scanners Information Retrieval Visualisierung Bildverarbeitung Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009175415&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lewmichaels principlesofvisualinformationretrieval |